Influenza A virus is a major human respiratory pathogen, and available vaccines and antivirals are of limited efficacy. In order to identify novel targets for therapeutic intervention during influenza virus infection, we have assembled an interdisciplinary team that uses a highly integrated systems level approach to identify and validate key genes/networks involved in virus pathogenesis. The overarching theme of our multidisciplinary proposal ?FluOMICS: The NEXT Generation? is to obtain multiple OMICS-based systems level measurements and integrate them using modeling approaches and machine learning algorithms to identify and validate 1) host-virus networks that modulate influenza A virus disease severity, 2) biomarkers in blood that reflect the activation states of these networks and 3) novel host targets for therapeutic interventions. The proposed studies leverage on our previous collaborations that generated global datasets and models that predict severity of disease caused by three specific influenza virus strains with different levels of pathogenicity. Our underlying main hypothesis is that host networks involved in viral replication and in early host responses regulate disease outcomes and represent promising targets for therapeutic intervention. We also propose that, in addition to the pathways identified in our previous collaboration, there are additional distinct pathways that result in either resilience or pathogenic outcomes after influenza virus infection, and that specific pathogenic pathways will require tailored therapeutic interventions. To identify networks associated with clinical disease in humans, we propose to integrate into predictive and comprehensive models OMICS responses during influenza virus infection in three systems 1) human blood from a human cohort of patients with documented influenza virus infection and diverse clinical outcomes (Project 1, Aim 3); 2) mouse blood and tissues from experimentally infected animals under a variety of conditions and perturbations resulting in diverse disease outcomes (Project 1, Aim 1) and 3) relevant primary human cells experimentally infected under controlled conditions and perturbations associated with diverse disease outcomes (Project 2). Samples will be collected, processed and send to the Technology Core to conduct global transcriptomics, proteomics and metabolomics analysis. OMICS data sets will be integrated and compared by the Modeling Core to generate network models of disease, uncover blood biomarkers and identify key drivers as targets for mechanistic studies and therapeutic intervention (Project 1, Aim 2). In summary, our systems modeling approaches will find correlates and associations between diverse experimental systems that will help us define human blood biomarkers, and link them to in vivo and ex vivo signatures for both companion diagnostics and personalized therapies.